Suppose you obtain the following regression model, E[y]=20+47*x +88*x^2. What is the impact of a 64 unit change of x on the expected value of y when x is at its mean of 57?

Suppose you obtain the following regression model, E[y]=20+47x +88x^2. What is the impact of a 64 unit change of x on the expected value of y when x is at its mean of 57?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A researcher is hired to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance in miles between home and work for the employees. A sample of 10 employees was chosen and the results are listed in the chart below. Use the proper Data Analysis function in Excel to computethe regression equation needed to predict the number of missed days that could be expected foremployees who live certain distances away from the workplace. Distance to Work (miles): 1, 3, 4, 6, 9, 10, 12, 14, 14, 18 Number of Days Absent: 9, 6, 7, 7, 6, 4, 5, 3, 4, 2
Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) Birthrate (number of births per 1000 people)
Consider an estimated regression explaining the salaries of CEOS(in millions of dollars) in terms of annual firm sales (in millions of dollars), return on equity (roe in percentage form), and return on fırm's stock (ros, in percentage form) : log(salary) = 4.32 + 0.018sales + 0.0174roe + 0.00024ros How would you interpret the estimated coefficient of sales ? When sales increase by 1 million dollars, CEOS' salary is expected to increase by 0.018%. When sales increase by 1 million dollars, CEOS' salary is expected to increase by 1.8%. When sales increase by 1%, CEOS' salary is expected to increase 0.18%. When sales increase by 100%, CEOS' salary is expected to increase 0.18%.
An investigator modeled the log-odds of getting stomach cancer as a function of number of servings of vegetables per week and the number of servings of red mean per week using logistic regression. The odds that a person gets stomach cancer if they have 4 servings of red meat a week are 1.2 times the odds of cancer for someone who has 3 servings of red meat a week. Suppose the fitted regression model is given as log-odds = -6 + 0.182 M – 0.105 V, where M = #servings of red meat per week and V = #servings of vegetables per week. What is the odds ratio representing the change in odds for an increase in one serving of vegetables? Give the answer to three decimal places.
The US government is interested in understanding what predicts death rates. They have a set of data that includes the number of deaths in each state, the number of deaths resulting from vehicle accidents (VEHICLE), the number of people dying from diabetes (DIABETES), the number of deaths related to the flu (FLU) and the number of homicide deaths (HOMICIDE). Your run a regression to predict deaths and get the following output: Which is not true of the coefficient of determination? A. It is the square of the coefficient of correlation B. It is negative when there is an inverse relationship between X and Y C. It reports the percentage of the variation in Y explained by X D. It is calculated using sums of squares (e.g., SSR, SSE, SST). R2 cannot be negative
Suppose that a researcher, using data on class size (CS) and average test scores from 102 third-grade classes, estimates the OLS regression TestScore=515.196+ (-5.7618)x CS, R² = 0.06, SER=11.4. A classroom has 23 students. The regression's prediction for that classroom's average test score is (Round your response to two decimal places.) Last year a classroom had 20 students, and this year it has 24 students. The regression's prediction for the change in the classroom average test score is (Round your response to two decimal places.) The sample average class size across the 102 classrooms is 21.19. The sample average of the test scores across the 102 classrooms is (Hint: Review the formulas for the OLS estimators.) (Round your response to two decimal places.)
A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking relate to the risk of strokes (Dataset "Stroke"). Risk is interpreted as the probability (times 100) that a person will have a stroke over the next 10-year period. For the smoker variable, 1 indicates a smoker and 0 indicates a nonsmoker. a. Develop an estimated regression equation that can be used to predict the risk of stroke given the age and blood-pressure level. b. Consider adding two independent variables to the model developed in part (a), one for the interaction between age and blood-pressure level and the other for whether the person is a smoker. Develop an estimated regression equation using these four independent variables. c. At a 0.05 level of significance, test to see whether the addition of the interaction term and the smoker variable contributes significantly to the estimated regression equation developed in part (a). d. Refer to the model developed in part…
Data was gathered from a random sample of young mothers between the ages of 15 and 19 years, and the relationship between the mother's age (measured in years) and the baby's birth weight (measured in grams) was observed to be linear, with r= 0.88. Further, the regression equation to predict a baby's birth welght based on the mother's age was found to be: Predicted birth weight = -1163.45 + 245.15(age). Based on this information, which one of the following statements is incorrect? O The predicted birth weight for a baby whose mother is 15 years old is 4840.7 grams. O If weight was measured in pounds instead of grams, the value of rwould still be 0.88. O If we switch the variables so that age is the response variable and birth weight is the explanatory variable, r would still be 0,88. O If a mother's age is 25 years, we would not want to use the regression equation to predict the baby's birth weight since this would be considered extrapolation. O Becauseris 0.88, we would consider the…
Suppose you ran a regression of AGE predicting INCOME (thousands of dollars). If your estimated line was INCOME = -1.5 + 1.2*AGE + ε, how much more income do you expect to make for every two years you age? (Express your answer in thousands of dollars.)
Calculate the estimate of σε What is the estimated resting heart rate for someone who is a smoker (Smoke=1) and exercises 1 hour a day (Exercise=1)?
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. What is the subpopulation mean when x = 7 servings per day?
Suppose a data set of 200 observations (n = 200) was analyzed using OLS to examine the relationship between CEO salary and various measures of firm performance. The regression results are as follows, with standard errors in parentheses: logy = 5 + 0.2logx₁0.03x₂ +0.002x3 (0.2) (0.04) (0.004) where? (0.009) y = CEO salary in thousands of dollars X₁ = annual firm sales X₂ = percentage of sales lost to competitors X3 = return on stock in percent R² = 0.290 Suppose you want to test the null hypothesis that percentage of sales lost to competitors has no ceteris paribus effect on the salaries of CEOS. For the one-sided alternative hypothesis ß₂ < 0 and 1% rejection rule (i.e., at the 1% level), you would_ the null hypothesis that ß₂ = 0.